PRIDE (PRediction In Dynamic Environments) is a hierarchical
multi-resolutional framework for moving object
prediction. PRIDE incorporates multiple prediction algorithms into a single, unifying framework. To date, we
have applied this framework to predict the future location of autonomous vehicles during on-road driving. In
this paper, we describe two different approaches to compute long-term predictions (on the order of seconds into
the future) within PRIDE. The first is a cost-based approach that uses a discretized set of vehicle motions and
costs associated with states and actions to compute probabilities of vehicle motion. The cost-based approach
is the first prediction approach we have been using within PRIDE. The second is a fuzzy-logic-based approach
that deals with the pervasive presence of uncertainty in the environment to negotiate complex traffic situations.
Using the high-fidelity physics-based framework for the Unified System for Automation and Robot Simulation
(USARSim), we will compare the performance of the two approaches in different driving situations at
traffic intersections. Consequently, we will show how the two approaches complement each other and how their
combination performs better than the cost-based approach only.